How to merge lines after HoughLinesP in opencv

Question

I have this code I am trying to wrap my head around that detect plant Crop-row using segmentation and HoughLine method.

I understand the segmentation and the HoughLineP part very well. But I get stuck on how to merge the lines outputted from the HoughLineP method.

My Question are

1. Why would we want to merge the lines detected by HoughlinesP method
2. What are the possible way to merge this line.. a link to any material would be greatly appreciated

I stumble on a similar question stackoverflow but there wasn't much explanation just bunch of code

without the merging part here is the output of the HoughlineP on the test image test_image

HoughLineP method

def draw_lines(image,mask):
    mask = mask*255
    mask = cv2.GaussianBlur(mask,(5,5),1)
    mask = cv2.Canny(mask.astype(np.uint8),80,255)
    lines = cv2.HoughLinesP(mask,1,np.pi / 180,threshold=50,
                        minLineLength=50,maxLineGap=250)
    lines = np.squeeze(lines, axis=1)
    m_lines = merge_lines(lines)
    for line in m_lines:
        x1, y1, x2, y2 = line.astype(int)
        cv2.line(image, (x1, y1), (x2, y2), (255, 0, 0), 2)
    
    return image

#merge lines that are near to each other based on distance

from numpy.polynomial import polynomial as P
def merge_lines(lines):
    clusters =  []
    idx = []
    total_lines = len(lines)
    if total_lines < 30:
        distance_threshold = 20
    elif total_lines <75:
        distance_threshold = 15
    elif total_lines<120:
        distance_threshold = 10
    else:
        distance_threshold = 7
    for i,line in enumerate(lines):
        x1,y1,x2,y2 = line
        if [x1,y1,x2,y2] in idx:
            continue
        parameters = P.polyfit((x1, x2),(y1, y2), 1)
        slope = parameters[0]#(y2-y1)/(x2-x1+0.001)
        intercept = parameters[1]#((y2+y1) - slope *(x2+x1))/2
        a = -slope
        b = 1
        c = -intercept
        d = np.sqrt(a**2+b**2)
    cluster = [line]
    for d_line in lines[i+1:]:
        x,y,xo,yo= d_line
        mid_x = (x+xo)/2
        mid_y = (y+yo)/2
        distance = np.abs(a*mid_x+b*mid_y+c)/d
        if distance < distance_threshold:
            cluster.append(d_line)
            idx.append(d_line.tolist())
    clusters.append(np.array(cluster))
    merged_lines = [np.mean(cluster, axis=0) for cluster in clusters]
    print(clusters)
    print(merged_lines)
    return merged_lines

I understand how the slope and intercept was obtain in the merging function.. but the rest of the code I don't understand.